CN111323719A - Method and system for online determination of health state of power battery pack of electric automobile - Google Patents
Method and system for online determination of health state of power battery pack of electric automobile Download PDFInfo
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
According to the online determination method and system for the health state of the power battery pack of the electric automobile, provided by the invention, the online estimation model for the health state of the power battery pack is adopted, the second peak value of the capacity increment curve of the power battery of the electric automobile can be determined and obtained according to the application data, and then the health state of the power battery pack of the electric automobile can be accurately obtained according to the second peak value. In addition, the online determination method and the online determination system for the health state of the power battery pack of the electric automobile can obtain the health state of the power battery pack of the electric automobile only according to the acquired application data, so that the determination process for the health state of the power battery pack of the electric automobile can be simplified, and the problem that the determination method for the health state of the power battery pack of the electric automobile in the prior art is difficult to apply is solved.
Description
Technical Field
The invention relates to the technical field of power battery management, in particular to a method and a system for online determining the health state of a power battery pack of an electric automobile.
Background
The widespread use of electric vehicles has been considered as a viable approach to reduce human dependence on fossil fuels and greenhouse gas emissions. Lithium ion batteries are widely used as energy storage devices in electric vehicles due to their advantages in energy density, cycle life, and the like. However, lithium ion batteries inevitably suffer from a degradation of their performance during their use, also known as aging, due to the presence of side reactions that occur continuously. Generally, a 100% increase in internal resistance or 20% capacity fade of a power cell is considered to reach its End of service life (EOL), and is no longer suitable for automotive applications. In addition, accurate metering of the capacity is also important for SOC estimation and fault diagnosis of the battery pack and remaining mileage prediction of the electric vehicle. However, the State of health (SOH) of the battery is difficult to measure during the driving of the vehicle due to test conditions and equipment limitations. Therefore, as one of the core tasks of a Battery Management System (BMS), accurate SOH estimation is a guarantee to improve the efficiency, safety, and durability of a battery system.
The existing lithium ion battery health state is defined mainly by a capacity definition method and an internal resistance definition method, and the calculation formulas are shown as a formula (1) and a formula (2).
In the formula, C0To an initial nominal capacity, CiThe i-th charging capacity.
In the formula: rnewOhmic internal resistance, R, of the battery when it leaves the factoryEOLOhmic internal resistance at end of battery life (EoL), RiThe measured ohmic internal resistance of the battery is the ith time.
When the SOH of the power battery pack of the electric vehicle is evaluated by using the capacity method, the power battery pack needs to be charged to a full-charge state by adopting a constant-current and constant-voltage mode (wherein the constant-current section generally selects 1/3C current multiplying power) under the condition of constant temperature, the power battery pack is kept still for 1 hour, then the battery is discharged to a cut-off voltage by adopting a constant-current (generally 1C) mode, the steps are circulated for at least 3 times, and the SOH is calculated by taking the average value of the stable discharge capacity of the battery pack as the maximum available capacity value of the current state. Not only is this method time consuming, but deep discharge can also be somewhat damaging to the battery.
In the actual test process, a pulse method and an electrochemical impedance spectroscopy are needed by the method for representing the SOH of the battery pack by using the internal resistance, on one hand, the internal resistance of the battery pack in actual application has small change amplitude along with aging and is not suitable for health state evaluation, and on the other hand, specific experimental equipment is needed for measuring the internal resistance, so that online estimation is difficult to realize.
In response to the above problems, many methods for battery SOH estimation have been proposed in the prior art, which can be roughly classified into three categories, i.e., physical-based methods, empirical methods, and data-driven methods. The physics-based approach uses Partial Differential Equations (PDEs) to describe battery dynamics that are closely related to battery aging, thereby enabling SOH estimation. The method can realize higher estimation precision, but a large amount of electrochemical parameters are required to be accurately measured, and the parameters cannot be accurately obtained; in addition, the model involves complicated partial differential calculations, and the amount of computation is extremely large, making it difficult to apply to an in-vehicle BMS. Experience-based methods describe battery dynamics using an equivalent circuit model and design a state observer to estimate the model parameters. However, the empirical battery model used lacks physical significance when the model is used under different working conditions of model fitting, and the model accuracy is limited, thus significantly reducing the SOH estimation accuracy. In recent years, data-driven methods such as gaussian process regression and support vector machines have received increasing attention because they do not require detailed procedures for understanding the electrochemical reactions during battery operation. In these methods, a large amount of test data is required to train the battery SOH model, and the non-linear relationship between the SOH and its influencing factors can be directly described.
The technical solutions similar to the present invention will now be described as follows. The invention patent with application number 201810504744.5 discloses a battery state of health analysis method and device. The method comprises the following steps: obtaining a relation curve of the SOC of the sample battery and the OCV of the sample battery under different SOHs; taking a relation curve of the SOC and the OCV of the sample battery as a reference curve when the SOH is a set value; the method comprises the steps that under different SOHs, a relation curve of the SOC and the OCV of a sample battery is subtracted from a reference curve, and a relation curve of the SOC and the open-circuit voltage variation delta OCV of the sample battery is obtained; analyzing a relation curve of the SOC and the delta OCV of the sample battery under different SOHs to obtain a slope k between two points in the relation curve of the SOC and the delta OCV; obtaining the corresponding relation between the SOH and the slope k according to the change of the slope k under different SOH; and analyzing to obtain the SOH of the lithium ion battery to be detected according to the value of the slope k of the lithium ion battery to be detected based on the corresponding relation between the SOH of the sample battery and the slope k. The method estimates the SOH through the difference between the OCV curve and the initial state after the battery is aged, theoretically, the SOH has reliability, however, the open-circuit voltage and the SOC are difficult to measure in real-vehicle application, and therefore errors exist in an algorithm.
The invention patent with application number 201610913062.0 discloses a method and a system for estimating the state of health of a battery on line, the method calculates the ratio of the capacity change to the voltage change in the charging and discharging process of the battery, and takes the maximum position as a reference point, and a voltage interval V is selected in the neighborhood of the reference point1+And V1-Filtering the change value of the volume in the interval and calculating the volume C in the intervalTSimultaneously measuring the actual capacity C of the battery in the current stateAAnd fitting C by linear regressionTAnd CAThe corresponding relationship between them, further pass CTValue to predict CAThe value is obtained. The method can realize the online estimation of the health state of the battery, but due to different types of batteries CTAnd CAMay have non-linearity, thereby causing a large error in the estimation result of the method。
The invention patent with the application number of 201810205365.6 discloses a health state estimation method based on a particle swarm optimization RBF neural network. Firstly, voltage, current and time data in the battery circulation process are collected, a capacity increment curve is drawn, capacity increment peak values and peak value position data are obtained after filtering and are used as input data, the actual health state of the battery is used as output, an RBF neural network model is established, and parameters of the neural network model are solved by applying a particle swarm optimization algorithm. The method can well establish the nonlinear relation between the capacity increment peak value and the battery health state, however, under the practical application working condition, the charging process is often incomplete, the voltage and current data are often low in precision, and a complete capacity increment curve is difficult to draw, so that the practical application is difficult.
Therefore, the present invention provides a method for determining the health status of a power battery pack of an electric vehicle, so as to solve the problems of inaccurate estimation and difficult application existing in the prior art in determining the health status of the power battery pack of the electric vehicle.
Disclosure of Invention
The invention aims to provide an online determination method and system for the health state of a power battery pack of an electric automobile, which solve the problems of inaccuracy in estimation and difficulty in application in the process of determining the health state of the power battery pack of the electric automobile in the prior art.
In order to achieve the purpose, the invention provides the following scheme:
an online determination method for the health state of a power battery pack of an electric automobile comprises the following steps:
acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile;
acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the online estimation model of the health state of the power battery pack according to the application data;
and determining the health state of the power battery pack of the electric automobile according to the second peak value.
Preferably, before the obtaining of the power battery pack state of health online estimation model with the application data as input and the second peak value of the capacity increment curve of the power battery of the electric vehicle as output, the method further comprises:
collecting application data of the electric automobile;
determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data;
using the collected application data and a second peak value corresponding to the application data as a data training pair;
and training the on-line estimation model of the health state of the power battery pack by adopting the data training pair.
Preferably, the determining, according to the collected application data, a second peak of a capacity increment curve of the electric vehicle power battery corresponding to the collected application data specifically includes:
obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method according to the acquired application data;
determining the terminal voltage and the charging capacity of the electric automobile according to the capacity-voltage curve;
fitting a capacity-voltage curve by adopting an SVR algorithm and taking the terminal voltage as input and the charging capacity as output to obtain a fitted capacity-voltage curve;
obtaining a capacity increment curve according to the fitted capacity-voltage curve, and determining a second peak value of the capacity increment curve; and the second peak value of the capacity increment curve is the second peak value of the capacity increment curve of the electric automobile power battery corresponding to the acquired application data.
Preferably, the acquiring application data of the electric vehicle further comprises:
collecting application data of the electric automobile;
preprocessing the acquired application data; the pretreatment comprises the following steps: mean value processing and absolute value processing.
An online health status determination system for a power battery pack of an electric vehicle, comprising:
the application data acquisition module is used for acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile;
the online estimation model acquisition module is used for acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
the first peak value determining module is used for determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the power battery pack health state online estimation model according to the application data;
and the health state determining module is used for determining the health state of the power battery pack of the electric automobile according to the second peak value.
Preferably, the system further comprises:
the first application data acquisition module is used for acquiring application data of the electric automobile;
the second peak value determining module is used for determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data;
the data training pair construction module is used for taking the acquired application data and a second peak value corresponding to the application data as a data training pair;
and the model training module is used for training the on-line estimation model of the health state of the power battery pack by adopting the data training pair.
Preferably, the second peak determining module specifically includes:
the capacity-voltage curve determining unit is used for obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method according to the acquired application data;
a terminal voltage and charging capacity determination unit for determining a terminal voltage and charging capacity of the electric vehicle according to the capacity-voltage curve;
the capacity-voltage curve fitting unit is used for fitting a capacity-voltage curve by adopting an SVR algorithm and taking the terminal voltage as input and the charging capacity as output to obtain a fitted capacity-voltage curve;
a peak value determining unit, configured to obtain a capacity incremental curve according to the fitted capacity-voltage curve, and determine a second peak value of the capacity incremental curve; and the second peak value of the capacity increment curve is the second peak value of the capacity increment curve of the electric automobile power battery corresponding to the acquired application data.
Preferably, the system further comprises:
the second application data acquisition module is used for acquiring application data of the electric automobile;
the preprocessing module is used for preprocessing the acquired application data; the pretreatment comprises the following steps: mean value processing and absolute value processing.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the online determination method and system for the health state of the power battery pack of the electric automobile, provided by the invention, the online estimation model for the health state of the power battery pack is adopted, the second peak value of the capacity increment curve of the power battery of the electric automobile can be determined and obtained according to the application data, and then the health state of the power battery pack of the electric automobile can be accurately obtained according to the second peak value. In addition, the online determination method and the online determination system for the health state of the power battery pack of the electric automobile can obtain the health state of the power battery pack of the electric automobile only according to the acquired application data, so that the determination process for the health state of the power battery pack of the electric automobile can be simplified, and the problem that the determination method for the health state of the power battery pack of the electric automobile in the prior art is difficult to apply is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a general flow diagram of the scheme provided in the provision of an embodiment of the present invention;
FIG. 2 is a flowchart of a method for determining the health status of a power battery pack of an electric vehicle on line according to an embodiment of the present invention;
FIG. 3 is a fitted capacity-voltage graph provided by an embodiment of the present invention;
FIG. 4 is a graph of IC curves at different mileage after filtering according to an embodiment of the present invention;
FIG. 5 is a graph of IC peak values versus accumulated mileage for all of the vehicles studied in an embodiment of the present invention;
FIG. 6 is a graph of the peak values of the IC after fitting in an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a radial basis function neural network model according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an online health status determining system for a power battery pack of an electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an online determination method and system for the health state of a power battery pack of an electric automobile, which solve the problems of inaccuracy in estimation and difficulty in application in the process of determining the health state of the power battery pack of the electric automobile in the prior art.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Interpretation of terms:
state Of Health (SOH) Of power battery: the method is characterized in that the ratio between the current performance state and the initial performance state of the power battery is represented, and a common representation method is represented by a capacity representation method and an internal resistance representation method.
Capacity increment Analysis (ICA): a method for extracting relevant features from a battery voltage curve using a ratio of a capacity differential to a voltage differential. Wherein, capacity Increment (IC)
Radial Basis Function Neural Network (RBFNN): a feed-forward neural network that uses radial basis functions as implicit neuron activation functions.
Battery Management System (BMS): typically including battery state estimation, thermal management, equalization, etc.
State Of Charge (SOC): the ratio of the remaining capacity of the battery to the current maximum available capacity.
Open Circuit Voltage (OCV): potential difference between the positive and negative electrodes of the battery in an electrochemical equilibrium state.
Constant Current Constant Voltage charging method (CCCV): the constant current charging is performed to the charging cut-off voltage specified by the battery manufacturer, and then the charging mode is converted into the constant voltage charging mode.
Charge-discharge rate (C rate): representing the ratio of charging current to rated capacity. For example, the 1/3C current represents the amount of current required to charge the battery to full charge for three hours.
Support Vector Regression (SVR): a machine learning method capable of fitting a nonlinear relationship with high accuracy.
Depth Of Discharge (DOD): battery discharge as a percentage of the rated capacity of the battery.
The overall design concept of the invention is shown in fig. 1, and specifically comprises the following steps: firstly, dividing the real vehicle running and charging data of the electric vehicle collected on a large data platform into charging and running segments, extracting effective charging segments, filling missing frames by an interpolation method, fitting a capacity-voltage curve by SVR support vector regression, drawing a capacity increment curve, smoothing the capacity increment curve by Gaussian window filtering, solving a second peak value, filling and regressing capacity increment peak values under different mileage by SVR (or least square regression, ridge regression and other modes), obtaining a complete capacity increment peak value change relation along with the mileage, and taking the processed capacity increment peak value as an output parameter of a health state estimation model. Next, the accumulated travel distance, the charge start SOC, the average charge current, and the average charge temperature are extracted from the charge segment, and the average travel temperature is extracted from the charge segment and used as an input parameter of the health state estimation model. And finally, establishing a big data-based power battery pack health state online estimation model by using a Radial Basis Function (RBFNN).
The data of 14 pure electric vehicles are trained by using the input and output parameters, and the data of the other 4 vehicles are verified, so that the result shows that the average error is 4%, and the model has better health state estimation capability.
The specific implementation scheme of the invention is as follows:
fig. 2 is a flowchart of a method for online determining a health state of a power battery pack of an electric vehicle according to an embodiment of the present invention, and as shown in fig. 2, the method for online determining a health state of a power battery pack of an electric vehicle includes:
s1, acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile; the accumulated driving mileage of the vehicle can reflect the ampere-hour throughput of the battery system in actual operation, and has a crucial influence on the degradation of the battery. The initial charge SOC is a direct reaction to DOD in the preamble trip.
S2, acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
s3, determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the online estimation model of the state of health of the power battery pack according to the application data;
and S4, determining the health state of the power battery pack of the electric automobile according to the second peak value.
In order to improve the accuracy of estimating the state of health of the power battery pack of the electric vehicle, before S2, the method may further include:
collecting application data of the electric automobile;
determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data;
using the collected application data and a second peak value corresponding to the application data as a data training pair;
and training the on-line estimation model of the health state of the power battery pack by adopting the data training pair.
The process of determining the second peak value of the capacity increment curve of the electric vehicle power battery corresponding to the acquired application data according to the acquired application data specifically comprises the following steps:
according to the collected application data, obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method, which specifically comprises the following steps:
in a charging segment, the charging capacity is calculated by the following formula:
wherein, Q is the charging capacity, i (T) is the charging current at the time T, and T is the sampling period.
Discretizing the equation (3), using k to represent the discrete time step, and further calculating the IC value of each voltage position:
wherein Q iskIndicates the charge capacity, V, of the battery at the k-th timekRepresenting the voltage of the battery at the k-th moment, Qk-1Represents the charge capacity, V, of the battery at the time k-1k-1Representing the voltage of the battery at time k-1.
Determining the terminal voltage and the charging capacity of the electric automobile according to the capacity-voltage curve;
however, due to the influence of the voltage measurement accuracy, it may occur that the measured voltage does not change in some consecutive time steps, as shown by the solid black line in fig. 3. This will lead to a zero denominator problem in the IC curve derivation. In order to solve the problem, a Support Vector Regression (SVR) algorithm (or machine learning regression models such as gaussian process regression and decision tree regression) is adopted to preprocess a data set (original data represented by a black solid line in fig. 3), that is, the SVR algorithm is adopted, the terminal voltage is used as input, the charging capacity is used as output, and a capacity-voltage curve is fitted to obtain a fitted capacity-voltage curve. This fitting process is specifically:
let the data set { (x)1，y1)，(x2，y2)，…，(xl，yl) In which xi∈RnIs a feature vector, yi∈ R is the target output, the system equation can be expressed as follows:
y＝ωξ+b,ω∈X,b∈R,
the SVR algorithm converts the equation into the following optimization problem:
in the formula, the vector ω is represented by C>0 is the model parameter of the regularization parameter,
relaxation variable representing the upper bound, ξiA relaxation variable, y, representing a lower limitiRepresenting the target output, xiA feature vector is represented. By solving the above equation, an approximation function can be derived:
wherein, αiAnd
are all Lagrangian operators, k (x)i,xc) Representing a kernel function. The invention applies a gaussian Radial Basis Function (RBF) whose expression is:
where k (.) represents the selected kernel, xiRepresenting sample points, xcRepresents the center point, σ is the standard deviation of the gaussian function, representing the width of the RBF kernel. In the battery charging process according to the present invention, the charged amount can be calculated by the following formula:
wherein, CrIs rated battery capacity, CkIs at VkCharged amount under voltage, k represents time step, s0Is an initial SOC value of a charging operation, IkIs the charging current and Δ T is the sampling interval.
It is worth mentioning that the calculated capacity at each specific voltage may deviate from the actual value due to the decrease of the actual battery capacity, however, the IC value calculation only needs to use the difference (increment) of the charging capacity between two sampling points, and thus has a limited influence on the IC curve derivation. The relationship between charge capacity and associated voltage may be approximated as
Ck＝f(Vk)
In the present invention, a capacity-voltage graph is obtained according to equation (11), as shown in fig. 4. The SVR algorithm is used to fit the capacity delta curve to eliminate the effects of raw data fluctuations. Wherein the input is terminal voltage VkOutput as charging capacity Ck。
Based on the SVR fitted capacity-voltage curve in fig. 3, the IC curve for each vehicle can be derived according to equation (4).
However, the accuracy of the voltage and current measurements inevitably affects the IC curve obtained, causing it to fluctuate abnormally. In order to effectively extract the features of the IC curve, a certain filtering algorithm needs to be adopted. In the present invention, Gaussian Window (GW) filtering (method or sliding mean filtering, low pass filtering, wavelet filtering, etc.) is used to smooth the IC curves, and the IC curves at different mileage after filtering are shown in fig. 5.
It can be seen that the second peak of the IC curve generally decreases as the accumulated mileage increases, and thus can be used to characterize the battery SOH. Namely, the health state of the power battery pack of the electric automobile is determined through the second peak value of the IC curve, and the second peak value can be determined after the capacity increment curve is obtained.
Fig. 5 depicts the peak evolution of different vehicles. It can be seen that all peak evolutions have similar patterns, but the slopes of the curves are different. By clustering, the vehicles under study can be divided into two groups. Therefore, classification factors are introduced to distinguish the categories of the vehicles (in the invention, the electric vehicles are divided into two categories according to the vehicle fading rate of the current electric vehicle by taking the specified fading rate as a boundary) so as to improve the prediction accuracy.
Further, due to the strict data requirements, not all voltage variation curves during charging can be used for IC curve derivation. In order to obtain the IC value for each charging process, it is necessary to use the SVR algorithm again to acquire the trend of the IC value evolution as the accumulated mileage increases, and the fitting result is shown in fig. 6. It can be seen that the fitted curve can adequately represent the evolution route of the IC values and filter out outliers. The fitted curve can be used to obtain the exact IC value for a particular accumulated mileage, and can be used as a model output for model training and validation.
In order to further improve the evaluation accuracy of the online determination method for the health state of the power battery pack of the electric vehicle, after the application data of the electric vehicle is acquired, the acquired application data needs to be preprocessed. The pretreatment specifically comprises: mean value processing and absolute value processing.
As another embodiment of the present invention, a Radial Basis Function Neural Network (RBFNN) model adopted by the present invention is specifically constructed as follows:
compared with a traditional Artificial Neural Network (ANN), the RBFNN model is mainly different in hidden layer number and activation function of input nodes. Because the activation function of the RBFNN model can map input variables to high dimensions, the nonlinear relation is converted into a linear relation, and the RBFNN model only needs one hidden layer. Therefore, the RBFNN model is more effectively trained than a general ANN, and the model training can be prevented from falling into a local minimum value.
The method utilizes the RBFNN model to establish an online estimation model of the health state of the power battery pack, and utilizes a gradient descent algorithm (or particle swarm optimization, a genetic algorithm and the like) to train and verify the model. The developed online estimation model of the health state of the power BATTERY pack can be embedded into a real BATTERY management system (BATTERY MANAGEMENT SYSTEM, BMS) for online calculation. The basic structure of the RBFNN neural network model is as follows:
wherein Y represents an output parameter, k represents the number of hidden nodes, and wjIs jththWeight between individual hidden node and output node, w0Is the bias term from hidden node to output node. The structure of the established model is shown in fig. 7. It consists of an input layer, a hidden layer and an output layer. The input layer contains six characteristic parameters, namely the accumulated driving mileage, the initial charging SOC, the average charging temperature, the average charging current, the average discharging temperature and the classification factor of the vehicle, and the second peak value of the IC curve is the only model output in the output layer.
The bp (back propagation) training algorithm has been widely used for the training of RBFNNs. Preferably, the present invention selects a Gradient Descent (Gradient) method to solve the fitting problem, and sets the learning rate to 0.001. Thus, the parameters in the above equation may be updated by:
xcj(i)＝xcj(i-1)+ηΔxcj+α(xcj(i-1)-xcj(i-2))
xcj(i)＝xcj(i-1)+ηΔxcj+α(xcj(i-1)-xcj(i-2))
xcj(i)＝xcj(i-1)+ηΔxcj+α(xcj(i-1)-xcj(i-2))
wherein, wjIs jththWeight of hidden node, σjIs jththStandard deviation of hidden node, xcjIs jththThe center value of each hidden node, i represents the number of iterations, η∈ [0,1 ]]α∈ [0,1 ] for learning rate]Is a momentum factor.
In addition, corresponding to the above online determination method for the health state of the power battery pack of the electric vehicle, the present invention further provides an online determination system for the health state of the power battery pack of the electric vehicle, as shown in fig. 8, the system includes: the system comprises an application data acquisition module 1, an online estimation model acquisition module 2, a first peak value determination module 3 and a health state determination module 4.
The application data acquisition module 1 is used for acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile;
the online estimation model acquisition module 2 is used for acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
the first peak value determining module 3 is used for determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the power battery pack health state online estimation model according to the application data;
and the health state determination module 4 is used for determining the health state of the power battery pack of the electric automobile according to the second peak value.
In order to further improve the accuracy of the heartburn prediction, the system further comprises: the device comprises a first application data acquisition module, a second peak value determination module, a data training pair construction module and a model training module.
The first application data acquisition module is used for acquiring application data of the electric automobile; the second peak value determining module is used for determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data; the data training pair construction module is used for taking the acquired application data and a second peak value corresponding to the application data as a data training pair; and the model training module is used for training the online estimation model of the health state of the power battery pack by adopting the data training pair.
The second peak determining module specifically includes: the device comprises a capacity-voltage curve determining unit, a terminal voltage and charging capacity determining unit, a capacity-voltage curve fitting unit and a peak value determining unit.
The capacity-voltage curve determining unit is used for obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method according to the acquired application data; the terminal voltage and charging capacity determining unit is used for determining the terminal voltage and charging capacity of the electric automobile according to the capacity-voltage curve; the capacity-voltage curve fitting unit is used for fitting a capacity-voltage curve by adopting an SVR algorithm and taking the terminal voltage as input and the charging capacity as output to obtain a fitted capacity-voltage curve; the peak value determining unit is used for obtaining a capacity increment curve according to the fitted capacity-voltage curve and determining a second peak value of the capacity increment curve; and the second peak value of the capacity increment curve is the second peak value of the capacity increment curve of the electric automobile power battery corresponding to the acquired application data.
In addition to the above components, the system provided by the present invention may further include: the system comprises a second application data acquisition module and a preprocessing module.
The second application data acquisition module is used for acquiring application data of the electric automobile; the preprocessing module is used for preprocessing the acquired application data; the pretreatment comprises the following steps: mean value processing and absolute value processing.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An online determination method for the health state of a power battery pack of an electric vehicle is characterized by comprising the following steps:
acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile;
acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the online estimation model of the health state of the power battery pack according to the application data;
and determining the health state of the power battery pack of the electric automobile according to the second peak value.
2. The method for determining the health state of the power battery pack of the electric vehicle on line according to claim 1, wherein the obtaining of the power battery pack health state on line estimation model with the application data as input and the second peak value of the capacity increment curve of the power battery of the electric vehicle as output further comprises:
collecting application data of the electric automobile;
determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data;
using the collected application data and a second peak value corresponding to the application data as a data training pair;
and training the on-line estimation model of the health state of the power battery pack by adopting the data training pair.
3. The method for determining the health state of the power battery pack of the electric vehicle on line according to claim 2, wherein the step of determining the second peak value of the capacity increment curve of the power battery of the electric vehicle corresponding to the collected application data according to the collected application data specifically comprises the steps of:
obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method according to the acquired application data;
determining the terminal voltage and the charging capacity of the electric automobile according to the capacity-voltage curve;
fitting a capacity-voltage curve by adopting an SVR algorithm and taking the terminal voltage as input and the charging capacity as output to obtain a fitted capacity-voltage curve;
obtaining a capacity increment curve according to the fitted capacity-voltage curve, and determining a second peak value of the capacity increment curve; and the second peak value of the capacity increment curve is the second peak value of the capacity increment curve of the electric automobile power battery corresponding to the acquired application data.
4. The method for determining the health status of the power battery pack of the electric vehicle as claimed in claim 1, wherein the obtaining of the application data of the electric vehicle further comprises:
collecting application data of the electric automobile;
preprocessing the acquired application data; the pretreatment comprises the following steps: mean value processing and absolute value processing.
5. An online health status determination system for a power battery pack of an electric vehicle, comprising:
the application data acquisition module is used for acquiring application data of the electric automobile; the application data includes: the method comprises the steps that the accumulated driving mileage value, the charging initial SOC value, the average charging current, the average driving temperature, the average charging temperature and the classification factor of the electric automobile are obtained; the classification factors are classified according to different decline rates of the electric automobile;
the online estimation model acquisition module is used for acquiring a power battery pack health state online estimation model which takes application data as input and takes a second peak value of a capacity increment curve of a power battery of the electric automobile as output;
the first peak value determining module is used for determining a second peak value of a capacity increment curve of the power battery of the electric automobile by utilizing the power battery pack health state online estimation model according to the application data;
and the health state determining module is used for determining the health state of the power battery pack of the electric automobile according to the second peak value.
6. The system for determining the state of health of the power battery pack of the electric vehicle as claimed in claim 5, further comprising:
the first application data acquisition module is used for acquiring application data of the electric automobile;
the second peak value determining module is used for determining a second peak value of a capacity increment curve of the electric automobile power battery corresponding to the acquired application data according to the acquired application data;
the data training pair construction module is used for taking the acquired application data and a second peak value corresponding to the application data as a data training pair;
and the model training module is used for training the on-line estimation model of the health state of the power battery pack by adopting the data training pair.
7. The system for determining the state of health of a power battery pack of an electric vehicle on line according to claim 6, wherein the second peak determination module specifically comprises:
the capacity-voltage curve determining unit is used for obtaining a capacity-voltage curve of the electric automobile by adopting an incremental capacity analysis method according to the acquired application data;
a terminal voltage and charging capacity determination unit for determining a terminal voltage and charging capacity of the electric vehicle according to the capacity-voltage curve;
the capacity-voltage curve fitting unit is used for fitting a capacity-voltage curve by adopting an SVR algorithm and taking the terminal voltage as input and the charging capacity as output to obtain a fitted capacity-voltage curve;
a peak value determining unit, configured to obtain a capacity incremental curve according to the fitted capacity-voltage curve, and determine a second peak value of the capacity incremental curve; and the second peak value of the capacity increment curve is the second peak value of the capacity increment curve of the electric automobile power battery corresponding to the acquired application data.
8. The system for determining the state of health of the power battery pack of the electric vehicle as claimed in claim 5, further comprising:
the second application data acquisition module is used for acquiring application data of the electric automobile;
the preprocessing module is used for preprocessing the acquired application data; the pretreatment comprises the following steps: mean value processing and absolute value processing.
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| CN202010191066.9A CN111323719A (en) | 2020-03-18 | 2020-03-18 | Method and system for online determination of health state of power battery pack of electric automobile |
| PCT/CN2021/081507 WO2021185308A1 (en) | 2020-03-18 | 2021-03-18 | Online determination method and system for state of health of power battery pack of electric vehicle |
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| CN202010191066.9A CN111323719A (en) | 2020-03-18 | 2020-03-18 | Method and system for online determination of health state of power battery pack of electric automobile |
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| CN114298616A (en) * | 2022-03-11 | 2022-04-08 | 西南石油大学 | Equipment health state evaluation method and device and computer equipment |
| CN115993538A (en) * | 2023-02-01 | 2023-04-21 | 上海玫克生储能科技有限公司 | Fitting method and device of battery capacity increment comprehensive curve and electronic equipment |
| CN115993538B (en) * | 2023-02-01 | 2023-10-20 | 上海玫克生储能科技有限公司 | Fitting method and device of battery capacity increment comprehensive curve and electronic equipment |
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